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  • 1
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    Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design
    Springer Science and Business Media LLC ; 2023
    In:  International Journal of Computational Intelligence Systems Vol. 16, No. 1 ( 2023-08-08)
    Details
    In: International Journal of Computational Intelligence Systems, Springer Science and Business Media LLC, Vol. 16, No. 1 ( 2023-08-08)
    Abstract: This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter ( $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 ) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.
    Type of Medium: Online Resource
    ISSN: 1875-6883
    URL: Article
    DOI: 10.1007/s44196-023-00304-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2754752-8
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  • 2
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    Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 10 ( 2022-05-16), p. 1696-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 10 ( 2022-05-16), p. 1696-
    Abstract: Remora Optimization Algorithm (ROA) is a recent population-based algorithm that mimics the intelligent traveler behavior of Remora. However, the performance of ROA is barely satisfactory; it may be stuck in local optimal regions or has a slow convergence, especially in high dimensional complicated problems. To overcome these limitations, this paper develops an improved version of ROA called Enhanced ROA (EROA) using three different techniques: adaptive dynamic probability, SFO with Levy flight, and restart strategy. The performance of EROA is tested using two different benchmarks and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of EROA.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10101696
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 3
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    EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 4 ( 2023-02-07), p. 851-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 4 ( 2023-02-07), p. 851-
    Abstract: The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solution’s quality, meanwhile, convergence speed is faster and the algorithm’s precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solution’s quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11040851
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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  • 4
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    Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems
    Elsevier BV ; 2023
    In:  Alexandria Engineering Journal Vol. 73 ( 2023-07), p. 543-577
    Details
    In: Alexandria Engineering Journal, Elsevier BV, Vol. 73 ( 2023-07), p. 543-577
    Type of Medium: Online Resource
    ISSN: 1110-0168
    URL: Article
    DOI: 10.1016/j.aej.2023.04.052
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2631413-7
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  • 5
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    Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels
    Institution of Engineering and Technology (IET) ; 2023
    In:  IET Generation, Transmission & Distribution Vol. 17, No. 14 ( 2023-07), p. 3211-3231
    Details
    In: IET Generation, Transmission & Distribution, Institution of Engineering and Technology (IET), Vol. 17, No. 14 ( 2023-07), p. 3211-3231
    Abstract: The Economic and Emission Dispatch (EED) method is widely used to optimize generator output in a power system. The goal is to reduce fuel costs and emissions, including carbon dioxide, sulphur dioxide, and nitrogen oxides, while maintaining power balance and adhering to limit constraints. EED aims to minimize emissions and operating costs while meeting power demands. To solve the multi‐objective EED problem, the supply‐demand optimization (SDO) algorithm is proposed, which employs a price penalty factor approach to convert it into a single‐objective function. The SDO algorithm uses a swarm‐based optimization strategy inspired by supply‐demand mechanisms in economics. The algorithm's performance is evaluated on seven benchmark functions before being used to simulate the EED problem on power systems with varying numbers of units and load demands. Established algorithms like the Grey Wolf Optimizer (GWO), Moth‐Flame Optimization (MFO), Transient Search Optimization (TSO), and Whale Optimization Algorithm (WOA) are compared to the SDO algorithm. The simulations are conducted on power systems with different numbers of units and load demands to optimize power generation output. The numerical analyses demonstrate that the SDO technique is more efficient and produces higher quality solutions than other recent optimization methods.
    Type of Medium: Online Resource
    ISSN: 1751-8687 , 1751-8695
    URL: Issue
    URL: Article
    DOI: 10.1049/gtd2.v17.14
    DOI: 10.1049/gtd2.12892
    Language: English
    Publisher: Institution of Engineering and Technology (IET)
    Publication Date: 2023
    detail.hit.zdb_id: 2264294-8
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  • 6
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    Dimensionality reduction approach based on modified hunger games search: case study on Parkinson’s disease phonation
    Springer Science and Business Media LLC ; 2023
    In:  Neural Computing and Applications Vol. 35, No. 29 ( 2023-10), p. 21979-22005
    Details
    In: Neural Computing and Applications, Springer Science and Business Media LLC, Vol. 35, No. 29 ( 2023-10), p. 21979-22005
    Abstract: Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinson’s disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).
    Type of Medium: Online Resource
    ISSN: 0941-0643 , 1433-3058
    URL: Article
    DOI: 10.1007/s00521-023-08936-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1136944-9
    detail.hit.zdb_id: 1480526-1
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  • 7
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    Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems
    Springer Science and Business Media LLC ; 2023
    In:  Soft Computing Vol. 27, No. 19 ( 2023-10), p. 13951-13989
    Details
    In: Soft Computing, Springer Science and Business Media LLC, Vol. 27, No. 19 ( 2023-10), p. 13951-13989
    Abstract: A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.
    Type of Medium: Online Resource
    ISSN: 1432-7643 , 1433-7479
    URL: Article
    DOI: 10.1007/s00500-023-08468-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1476598-6
    SSG: 11
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  • 8
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    Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method
    Springer Science and Business Media LLC ; 2023
    In:  Engineering with Computers Vol. 39, No. 3 ( 2023-06), p. 1935-1979
    Details
    In: Engineering with Computers, Springer Science and Business Media LLC, Vol. 39, No. 3 ( 2023-06), p. 1935-1979
    Type of Medium: Online Resource
    ISSN: 0177-0667 , 1435-5663
    URL: Article
    DOI: 10.1007/s00366-021-01542-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1459031-1
    detail.hit.zdb_id: 51529-2
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  • 9
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    A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation
    Elsevier BV ; 2022
    In:  Expert Systems with Applications Vol. 209 ( 2022-12), p. 118272-
    Details
    In: Expert Systems with Applications, Elsevier BV, Vol. 209 ( 2022-12), p. 118272-
    Type of Medium: Online Resource
    ISSN: 0957-4174
    URL: Article
    DOI: 10.1016/j.eswa.2022.118272
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1041179-3
    detail.hit.zdb_id: 2017237-0
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  • 10
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    Single and Multi-Objectives for Simultaneous Integration of Capacitors and Multi-Type Dgs in Distribution Systems: Development and Implementation of a Novel Golden Jackal Optimization Algorithm
    Elsevier BV ; 2022
    In:  SSRN Electronic Journal
    Details
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    URL: Article
    DOI: 10.2139/ssrn.4274779
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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